Abstract
Here, we present a novel approach called the “parameter identification of complex network dynamics” algorithm which combines elements of the sparse identification of nonlinear dynamics algorithm with a genetic algorithm to automatically and efficiently discover the underlying dynamics of complex networks from data with minimal domain-specific knowledge requirements. Testing the proposed algorithm on empirical complex network data verifies the accuracy and efficiency of this method compared to a purely evolutionary approach.
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